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Argo Floats android application¶

Analytics stuff¶

In [1]:
import pandas as pd
import glob, folium, branca, json
import numpy as np
import matplotlib.pyplot as plt

import holoviews as hv
import panel as pn
import panel.widgets as pnw

hv.extension('bokeh')
In [2]:
from bokeh.themes.theme import Theme

theme = Theme(
    json={
'attrs' : {
    'Figure' : {
        'background_fill_color': '#535353',
        'border_fill_color': '#535353',
        'outline_line_color': '#444444',
    },
    'Grid': {
        'grid_line_dash': [6, 4],
        'grid_line_alpha': .3,
    },

    'Axis': {
        'major_label_text_color': 'white',
        'axis_label_text_color': 'white',
        'major_tick_line_color': 'white',
        'minor_tick_line_color': 'white',
        'axis_line_color': "white"
    }
  }
})
hv.renderer('bokeh').theme = theme
In [3]:
print(np.datetime64('now'))

2022-01-16T00:47:43
In [4]:
# PUT EVERY COUNTRY REPORTS IN ONE DATAFRAME
files = glob.glob('data/install*country.csv')
files.sort()
ds=pd.DataFrame()
for f in files:
    ds = pd.concat([ds,pd.read_csv(f,encoding = 'utf-16')])
ds = ds.reset_index()    
ds = ds.drop(columns=['index','Daily Device Upgrades','Total User Installs','Active Device Installs','Install events','Update events','Uninstall events'])
ds['Date']=pd.DatetimeIndex(ds['Date'])
ds['WO-ui']=ds['Daily User Installs'].cumsum()
ds['WO-di']=ds['Daily Device Installs'].cumsum()
ds['WO-uu']=ds['Daily User Uninstalls'].cumsum()
ds['WO-du']=ds['Daily Device Uninstalls'].cumsum()
In [5]:
countries = np.unique(ds['Country'][~ds['Country'].isna()])
for country in np.unique(ds['Country'][~ds['Country'].isna()]):
    ds[country+'-di']=ds[ds['Country']==country]['Daily Device Installs'].cumsum()
    ds[country+'-ui']=ds[ds['Country']==country]['Daily User Installs'].cumsum()
    ds[country+'-du']=ds[ds['Country']==country]['Daily Device Uninstalls'].cumsum()
    ds[country+'-uu']=ds[ds['Country']==country]['Daily User Uninstalls'].cumsum()
ds = ds.fillna(method='ffill')
ds = ds.fillna(0)
ds.tail()
Out[5]:
Date Package Name Country Daily Device Installs Daily Device Uninstalls Daily User Installs Daily User Uninstalls WO-ui WO-di WO-uu ... UA-du UA-uu US-di US-ui US-du US-uu UZ-di UZ-ui UZ-du UZ-uu
7074 2022-01-12 com.kb.android.argo JP 0 0 0 0 122 135 80 ... 0.0 1.0 7.0 5.0 0.0 2.0 1.0 1.0 0.0 1.0
7075 2022-01-12 com.kb.android.argo PA 0 0 0 0 122 135 80 ... 0.0 1.0 7.0 5.0 0.0 2.0 1.0 1.0 0.0 1.0
7076 2022-01-12 com.kb.android.argo PL 0 0 0 0 122 135 80 ... 0.0 1.0 7.0 5.0 0.0 2.0 1.0 1.0 0.0 1.0
7077 2022-01-12 com.kb.android.argo RU 1 0 1 1 123 136 81 ... 0.0 1.0 7.0 5.0 0.0 2.0 1.0 1.0 0.0 1.0
7078 2022-01-12 com.kb.android.argo US 0 0 0 0 123 136 81 ... 0.0 1.0 7.0 5.0 0.0 2.0 1.0 1.0 0.0 1.0

5 rows × 175 columns

Installations cumulation¶

In [6]:
A2 = pd.read_csv('A2codes.csv',index_col=1)
A2i = pd.read_csv('A2codes.csv',index_col=0)

labels = sorted([A2['Name'][x] for x in np.hstack([countries,'WO'])])

label = pnw.Select(name='Country', value='World', options=labels)
ptype = pnw.Select(name='Type', value='Install', options=['Install','Uninstall'])


@pn.depends(label.param.value,ptype.param.value) 
def create_figure(label,ptype):
    code = A2i['Code'][label]  
    if code != 'WO':
        dsi = ds[ds['Country']==code]
    else:
        dsi=ds
    
    hv_data = hv.Table(dsi, ['Date'])    
    p1 = hv_data.to.curve(['Date'], [code+'-u'+ptype[0].lower()],label='Users').opts(color='#2acaea')
    p2 = hv_data.to.curve(['Date'], [code+'-d'+ptype[0].lower()],label='Devices').opts(color='#d71e3e')      
    p = p1*p2
    p.opts(hv.opts.Curve(width=700, height=400,show_grid=True),
           hv.opts.Overlay(legend_position='top_left'))
    
    return p

#Panel dataframe looks better than holoview's
@pn.depends(label.param.value,ptype.param.value)
def create_table(label,ptype):
    code = A2i['Code'][label]  
    if code != 'WO':
        dsi = ds[ds['Country']==code]
    else:
        dsi=ds
    return pnw.DataFrame(dsi[['Date',code+'-u'+ptype[0].lower(),code+'-d'+ptype[0].lower()]].groupby('Date').max(),height=400, widths=180, autosize_mode='none')


widgets = pn.WidgetBox(label, ptype, width=170)
pn.Row(widgets, create_figure, create_table)
Out[6]:

World choropleth from install¶

In [7]:
df = pd.DataFrame(ds.groupby('Country').sum()['Daily User Installs'])
color_scale = branca.colormap.linear.viridis.scale(0,20)
map_dict = df.to_dict()

def get_color(feature):
    value = map_dict['Daily User Installs'].get(feature['properties']['ISO_A2'])
    if value is None:
        return 'white' # MISSING -> white
    else:
        #print(feature['properties']['ADMIN']+' : '+str(value))
        return color_scale(value)

m = folium.Map(
    location = [0, 0], 
    tiles="cartodbpositron",
    zoom_start = 2
)

folium.GeoJson(
    data = 'countries.json',
    style_function = lambda feature: {
        'fillColor': get_color(feature),
        'fillOpacity': 0.7,
        'color' : 'None',
        'weight' : 1,
    }    
).add_to(m)
m.add_child(color_scale)
m
Out[7]:
Make this Notebook Trusted to load map: File -> Trust Notebook